Privacy protection in government data sharing: an improved LDP-based approach

Piao, Chunhui; Hao, Yurong; Yan, Jiaqi; Jiang, Xuehong

doi:10.1007/s11761-021-00315-3

Cited by 8 publications

(3 citation statements)

References 35 publications

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“…Finally, the clustering results are anonymized using generalization techniques to ensure data privacy. In addition, Piao et al 15 proposed an improved local difference privacy (LDP)‐based scheme. Data chunking technique and count mean sketch (CMS) algorithm were adopted in this scheme.…”

Section: Related Workmentioning

confidence: 99%

A group key exchange and secure data sharing based on privacy protection for federated learning in edge‐cloud collaborative computing environment

et al. 2023

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SummaryFederated learning (FL) is widely used in internet of things (IoT) scenarios such as health research, automotive autopilot, and smart home systems. In the process of model training of FL, each round of model training requires rigorous decryption training and encryption uploading steps. The efficiency of FL is seriously affected by frequent encryption and decryption operations. A scheme of key computation and key management with high efficiency is urgently needed. Therefore, we propose a group key agreement technique to keep private information and confidential data from being leaked, which is used to encrypt and decrypt the transmitted data among IoT terminals. The key agreement scheme includes hidden attribute authentication, multipolicy access, and ciphertext storage. Key agreement is designed with edge‐cloud collaborative network architecture. Firstly, the terminal generates its own public and private keys through the key algorithm then confirms the authenticity and mapping relationship of its private and public keys to the cloud server. Secondly, IoT terminals can confirm their cryptographic attributes to the cloud and obtain the permissions corresponding to each attribute by encrypting the attributes. The terminal uses these permissions to encrypt the FL model parameters and uploads the secret parameters to the edge server. Through the storage of the edge server, these ciphertext decryption parameters are shared with the other terminal models of FL. Finally, other terminal models are trained by downloading and decrypting the shared model parameters for the purpose of FL. The performance analysis shows that this model has a better performance in computational complexity and computational time compared with the cited literature.

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Section: Related Workmentioning

confidence: 99%

A group key exchange and secure data sharing based on privacy protection for federated learning in edge‐cloud collaborative computing environment

et al. 2023

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“…The POR mechanism was first proposed by Jules et al [11]. In practical applications, the POR mechanism is mostly adopted in the integrity auditing of some essential information, such as confidential data from the military, government, biomedical and scientific research institutes, and other confidential units that are stored [12]. Because the POR mechanism can not only give access if the data in the cloud have already been corrupted or altered, but it can also partially recover the damaged data.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Data Integrity Auditing Based on Hierarchical Merkle Hash Tree in Cloud Storage

et al. 2023

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In cloud storage mode, users lose physical control over their data. To enhance the security of outsourced data, it is vital to audit the data integrity of the data owners. However, most of the current audit protocols have a single application scenario and cannot accommodate the actual needs of individuals and enterprises. In this research, a safe and efficient auditing scheme is proposed that is based on a hierarchical Merkle tree. On the one hand, we use a hierarchical authentication data structure and local signature aggregation technique to reduce the scale of the Merkle tree. In addition, authoritative nodes are introduced to reduce the length of the authentication path and improve the update efficiency. On the other hand, we introduce a monitoring mechanism that is based on the original data integrity auditing model to analyze the cloud data, which improves the transparency and credibility of cloud service providers. In addition, we achieve incomplete data recovery through log analysis, which greatly reduces the number of replicas of files under the premise of multi-copy auditing, reduces the burden on cloud service providers, and improves the fairness of audit protocols. The theoretical analysis and experimental comparison prove that the method is secure and efficient. It can effectively reduce the computational overhead and storage overhead in integrity auditing.

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“…The analysis, mining, and application of massive data have attracted great attention to governments, industries, and research departments, etc [ 1 ]. It is not rare that health sectors and hospitals may share patient details with organizations such as research institutions for further analysis [ 2 , 3 ]. Although data sharing has given us convenience, it may also bring challenges in privacy and ethics.…”

Section: Introductionmentioning

confidence: 99%

K-Anonymity Privacy Protection Algorithm for Multi-Dimensional Data against Skewness and Similarity Attacks

Huang

Miao

et al. 2023

Sensors

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Currently, a significant focus has been established on the privacy protection of multi-dimensional data publishing in various application scenarios, such as scientific research and policy-making. The K-anonymity mechanism based on clustering is the main method of shared-data desensitization, but it will cause problems of inconsistent clustering results and low clustering accuracy. It also cannot defend against several common attacks, such as skewness and similarity attacks at the same time. To defend against these attacks, we propose a K-anonymity privacy protection algorithm for multi-dimensional data against skewness and similarity attacks (KAPP) combined with t-closeness. Firstly, we propose a multi-dimensional sensitive data clustering algorithm based on improved African vultures optimization. More specifically, we improve the initialization, fitness calculation, and solution update strategy of the clustering center. The improved African vultures optimization can provide the optimal solution with various dimensions and achieve highly accurate clustering of the multi-dimensional dataset based on multiple sensitive attributes. It ensures that multi-dimensional data of different clusters are different in sensitive data. After the dataset anonymization, similar sensitive data of the same equivalence class will become less, and it eventually does not satisfy the premise of being theft by skewness and similarity attacks. We also propose an equivalence class partition method based on the sensitive data distribution difference value measurement and t-closeness. Namely, we calculate the sensitive data distribution’s difference value of each equivalence class and then combine the equivalence classes with larger difference values. Each equivalence class satisfies t-closeness. This method can ensure that multi-dimensional data of the same equivalence class are different in multiple sensitive attributes, and thus can effectively defend against skewness and similarity attacks. Moreover, we generalize sensitive attributes with significant weight and all quasi-identifier attributes to achieve anonymous protection of the dataset. The experimental results show that KAPP improves clustering accuracy, diversity, and anonymity compared to other similar methods under skewness and similarity attacks.

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Privacy protection in government data sharing: an improved LDP-based approach

Cited by 8 publications

References 35 publications

A group key exchange and secure data sharing based on privacy protection for federated learning in edge‐cloud collaborative computing environment

A group key exchange and secure data sharing based on privacy protection for federated learning in edge‐cloud collaborative computing environment

Dynamic Data Integrity Auditing Based on Hierarchical Merkle Hash Tree in Cloud Storage

K-Anonymity Privacy Protection Algorithm for Multi-Dimensional Data against Skewness and Similarity Attacks

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